Deep learning for intrusion detection systems (IDS) is a prominent research area focused on leveraging neural network architectures to detect malicious activities, anomalies, and cyber attacks in network traffic and systems. Research papers in this domain explore deep learning models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory networks (LSTMs), gated recurrent units (GRUs), autoencoders, and hybrid architectures for real-time and accurate intrusion detection. Key contributions include feature extraction from network flows, packet headers, system logs, and IoT device data, handling imbalanced datasets, and improving detection of zero-day attacks and advanced persistent threats. Recent studies also address challenges like computational efficiency, scalability to large-scale networks, adversarial attacks, and deployment on cloud or edge/fog computing platforms for low-latency detection. By applying deep learning, research in IDS aims to provide adaptive, robust, and high-accuracy security solutions to safeguard networks, IoT infrastructures, and enterprise systems against evolving cyber threats.